CODEWITHSUNDEEP
tensorflowtensorboard
golmschenk
golmschenk

Reputation: 12404

TensorFlow - Removing name scope when plotting summary?

I'm currently building my operations twice, once for training and once for validation with the variable_scope set to have reuse=True to ensure I've only got one set of weights to train.

To organize the operations though, I wrap the operation building call for training in a

with tf.name_scope='train':

and similarly do the same for validation. This allows me to create a few summary hooks easily, by simply calling 

tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES, scope='train'))

at the end to get summaries for either the training graph or the validation graph and save these summaries with the appropriate summary saver.

Unfortunately, this also means that a scalar in the training summaries is not displayed on the same plot as the equivalent scalar in the validation (because they are in different name scopes).

Is there either a way to remove the name scope before saving the summary, or a different method of wrapping the summaries for a specific case together without applying the name scope to begin with? Or do I need to manually keep track of the summaries for each case?

EDIT:

Just clarify, my code looks something like:

with tf.name_scope('train'):
    create_network()  # Summaries create in here.
with tf.name_scope('validation'):
    create_network(reuse=True)  # More summaries in here.
train_summaries = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES, scope='train'))
validation_summaries = tf.summary.merge(tf.get_collection(tf.GraphKeys.SUMMARIES, scope='validation'))
# Down here, create the summary saver hooks, etc.

Upvotes: 2

Views: 1062

Answers (1)

musically_ut
musically_ut

Reputation: 34288

Something like this is done in the multi-GPU CIFRA-10 example code to get rid of unnecessary prefixes:

loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)
tf.summary.scalar(loss_name, l)

Perhaps you can report the scalar with the same name from both validation as well as the training part of your code.

Upvotes: 1

Related Questions